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Balasubramaniam Natarajan - IEEE Xplore Author Profile
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Balasubramaniam Natarajan

Also published under: B. Natarajan, Bala Natarajan, Balasubramaniam Bala Natarajan, Balasubramanium Natarajan

Publications
256
Citations
3,060
Publications by Year
19862025
Co-Authors:
Show All Co-Authors (254)
Image of author Balasubramaniam Natarajan

Balasubramaniam Natarajan

Also published under: B. Natarajan, Bala Natarajan, Balasubramaniam Bala Natarajan, Balasubramanium Natarajan

Affiliation

Mike Wiegers Department of Electrical and Computer Engineering
Kansas State University
Manhattan, KS, USA

Publication Topics

Biography

Bala Natarajan (SM’08) received the B.E. degree in electrical engineering from the Birla Institute of Technology and Science, Pilani, India, in 1997, and the Ph.D. degree in electrical engineering from Colorado State University, Fort Collins, CO, USA, in 2002. Since Fall 2002, he has been a Faculty Member with the Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA, where he is currently the Clair N. Palmer and Sara M. Palmer Professor and the Director of the Wireless Communication and Information Processing Research Group. His research interests include spread spectrum communications, multicarrier CDMA and OFDM, multiuser detection, cognitive radio networks, sensor signal processing, distributed d...
Publications
256
Citations
3,060
Publications by Year
19862025
Co-Authors:
Show All Co-Authors (254)
Author's Published Works

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Meaningful Goals: Toward a Semantic-Functional Rate Distortion Theory

Dylan Wheeler;Balasubramaniam Natarajan

IEEE Wireless Communications Letters
Year: 2025 | Early Access Article |
While semantic communication has become an active area of research, a unifying theoretical foundation remains elusive. In this letter, we aim to partly fill this gap by providing a new perspective on goal-oriented semantic rate distortion theory. We extend the classical rate distortion model to include a semantic encoder and decoder, and define achievability with respect to the task distortion. We...Show More

Meaningful Goals: Toward a Semantic-Functional Rate Distortion Theory

Dylan Wheeler;Balasubramaniam Natarajan

Year: 2025 | Early Access Article |

Online Bad Data Detection in Compressed Sensing Based Distribution System State Estimation

James Ranjith Kumar Rajasekaran;Balasubramaniam Natarajan;Jing Jiang

IEEE Transactions on Smart Grid
Year: 2025 | Early Access Article |
This letter introduces a novel approach for online bad data detection in distribution system state estimation (DSSE) by integrating compressive sensing (CS) with a modified largest normalized residual (LNR)-based detector. To the best of the authors’ knowledge, this is the first work to develop a bad data detection method specifically for CS-based DSSE in unobservable distribution networks. The pa...Show More

Online Bad Data Detection in Compressed Sensing Based Distribution System State Estimation

James Ranjith Kumar Rajasekaran;Balasubramaniam Natarajan;Jing Jiang

Year: 2025 | Early Access Article |

Conceptual Learning and Causal Reasoning for Semantic Communication

Dylan Wheeler;Balasubramaniam Natarajan

IEEE Transactions on Cognitive Communications and Networking
Year: 2025 | Early Access Article |
Semantic communication is a paradigm shift toward meaning-oriented communication that is largely enabled by artificial intelligence technologies. One approach to semantic communication that leverages this intelligence is based on the theory of conceptual spaces. However, learning these conceptual spaces in an automated fashion is a major challenge. Moreover, the true intelligence of semantic commu...Show More

Conceptual Learning and Causal Reasoning for Semantic Communication

Dylan Wheeler;Balasubramaniam Natarajan

Year: 2025 | Early Access Article |

A Multi-Modal Smart Switching Based Image Transmission Using Semantic Communication

Sanjana Tiwari;Sharda Tripathi;Sandeep Joshi;Priyanshu Bhatt;Balasubramaniam Natarajan

2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS)
Year: 2025 | Conference Paper |
The conventional paradigm of communication primarily concentrates on the transmission of raw data, often disregarding its contextual meaning. However, to tackle the exponential growth in data demands along with the limited availability of transmission bandwidth, there is an increasing need to transition from Shannon’s classical information-theoretic communication to a more advanced framework cente...Show More

A Multi-Modal Smart Switching Based Image Transmission Using Semantic Communication

Sanjana Tiwari;Sharda Tripathi;Sandeep Joshi;Priyanshu Bhatt;Balasubramaniam Natarajan

Year: 2025 | Conference Paper |

Revamped Lane detection in hazy environments using Denoising Diffusion Probabilistic Model

Sivakumar Palanirajan;B Natarajan;Arun Baalaji;Ebin Joe

2024 International Conference on Modeling, Simulation & Intelligent Computing (MoSICom)
Year: 2024 | Conference Paper |
In today’s world of rapidly growing technological markets, image processing has become vital for systems to work on visual data. A noteworthy task would be lane detection in autonomous vehicles. A good lane detection system should be able to perform the processing irrespective of the environment and this mandates that adequate preprocessing should be done. One such preprocessing step is image deha...Show More

Revamped Lane detection in hazy environments using Denoising Diffusion Probabilistic Model

Sivakumar Palanirajan;B Natarajan;Arun Baalaji;Ebin Joe

Year: 2024 | Conference Paper |

Enhanced Corrosion Detection Using YOLO-Based Deep Learning Models

J Mukundh;Angad Rehsi;U Vishveshwara;B Natarajan

2024 International Conference on Modeling, Simulation & Intelligent Computing (MoSICom)
Year: 2024 | Conference Paper |
Corrosion is a persistent issue for maintenance teams across industries. Any corrosion left undetected can cause significant damage to infrastructure, equipment and machinery posing a threat to the safety of the workers and crews leading to substantial repairs. Standard corrosion detection methods relying on manual inspection by workers are found to be insufficient, being slow and often overlookin...Show More

Enhanced Corrosion Detection Using YOLO-Based Deep Learning Models

J Mukundh;Angad Rehsi;U Vishveshwara;B Natarajan

Year: 2024 | Conference Paper |

Predictive Modeling for Cirrhosis Diagnosis: A Machine Learning Exploration

Sudeesh Kumar V;B. Natarajan;Murali P;Prabu Selvam;Venkatraman K;N.R. Nagarajan

2024 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC)
Year: 2024 | Conference Paper |
Cited by: Papers (4)
Cirrhosis manifests as a condition wherein healthy liver tissue is gradually replaced by scar tissue, resulting in a decline in liver function. This ailment frequently correlates with autoimmune disorders, non-alcoholic fatty liver disease, excessive alcohol intake, and persistent liver conditions such as hepatitis B or C. As cirrhosis progresses, it can cause liver failure and various complicatio...Show More

Predictive Modeling for Cirrhosis Diagnosis: A Machine Learning Exploration

Sudeesh Kumar V;B. Natarajan;Murali P;Prabu Selvam;Venkatraman K;N.R. Nagarajan

Year: 2024 | Conference Paper |

A Novel Approach for Flower Classification using Deep Networks

A Jeevan Reddy;B Natarajan;A Abinaya;M Tamilselvi;P Murali;K Geetha

2024 Second International Conference on Data Science and Information System (ICDSIS)
Year: 2024 | Conference Paper |
The introduction of transformer models, which utilize a self-attention mechanism within deep neural networks, represents a notable breakthrough in natural language processing. This advancement has spurred researchers to investigate its applicability in computer vision tasks. Transformer-based models have showcased remarkable performance compared to traditional convolutional and recurrent neural ne...Show More

A Novel Approach for Flower Classification using Deep Networks

A Jeevan Reddy;B Natarajan;A Abinaya;M Tamilselvi;P Murali;K Geetha

Year: 2024 | Conference Paper |

Autoencoder-Based Domain Learning for Semantic Communication with Conceptual Spaces

Dylan Wheeler;Balasubramaniam Natarajan

2024 Wireless Telecommunications Symposium (WTS)
Year: 2024 | Conference Paper |
Communication with the goal of accurately conveying meaning, rather than accurately transmitting symbols, has become an area of growing interest. This paradigm, termed semantic communication, typically leverages modern developments in artificial intelligence and machine learning to improve the efficiency and robustness of communication systems. However, a standard model for capturing and quantifyi...Show More

Autoencoder-Based Domain Learning for Semantic Communication with Conceptual Spaces

Dylan Wheeler;Balasubramaniam Natarajan

Year: 2024 | Conference Paper |

Safeguarding the Homeless Using ViTransformer in Violence Detection

R Bhuvaneswari;Sai Lasya M;B Natarajan;Tamilselvi M;K Geetha;M Velammal

2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
In recent years, the issue of homelessness has gained significant attention as societies grapple with finding solutions to alleviate the plight of those without stable shelter. To address this pressing concern, this research utilizes advanced video analysis techniques, specifically Vision Transformer, to detect the presence of individuals in homeless populations. By leveraging state-of-the-art tec...Show More

Safeguarding the Homeless Using ViTransformer in Violence Detection

R Bhuvaneswari;Sai Lasya M;B Natarajan;Tamilselvi M;K Geetha;M Velammal

Year: 2024 | Conference Paper |

GNN-based Criticality Analysis in Interconnected Infrastructure Networks

Priyanka Gautam;Balasubramaniam Natarajan

2024 IEEE Green Technologies Conference (GreenTech)
Year: 2024 | Conference Paper |
This paper proposes a learning-based approach to identify vulnerabilities in interconnected infrastructure networks. For the first time, in contrast to purely network-based metrics (like degree and eigenvector centrality), our approach incorporates performance-based feature metrics in vulnerability assessment. Our primary goal is the creation of a scalable, adaptable framework for vulnerability an...Show More

GNN-based Criticality Analysis in Interconnected Infrastructure Networks

Priyanka Gautam;Balasubramaniam Natarajan

Year: 2024 | Conference Paper |

Revolutionizing Influenza A Detection in Humans: Unleashing the Power of XGBoost Classification

V Nikhil;S A Sahaaya Arul Mary;B Natarajan;A. Bajulunisha;R.S. Anuradha;K Venkatraman

2024 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE)
Year: 2024 | Conference Paper |
Cited by: Papers (4)
Influenza A, a zoonotic virus potentially affecting and infecting humans, poses a significant global health threat. This research paper presents a comprehensive study on predicting Influenza A outbreaks by applying the XGBoost classification algorithm. The study utilizes two substantial datasets: one from the World Health Organization (WHO) containing global Influenza A epidemiological data from 2...Show More

Revolutionizing Influenza A Detection in Humans: Unleashing the Power of XGBoost Classification

V Nikhil;S A Sahaaya Arul Mary;B Natarajan;A. Bajulunisha;R.S. Anuradha;K Venkatraman

Year: 2024 | Conference Paper |

Optimizing Energy Usage and Data Storage in Industrial IoT Settings Through Heuristic-Based Data-Rate Processors

B. Bharathi Kannan;Srinivasan Sriramulu;Vidhya Gopal;M Karthick;Yanna Nithin Reddy;B Natarajan

2024 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE)
Year: 2024 | Conference Paper |
The Internet of Things (IoT) is a fast-expanding field, with applications in areas as diverse as transportation, aviation, automation, electricity, and healthcare. Sensors, protocols, actuators, cloud services, and layers are just few of the components that make up an IoT architecture. The architecture of the Internet of Things is crucial in delivering the goods. The research community now provide...Show More

Optimizing Energy Usage and Data Storage in Industrial IoT Settings Through Heuristic-Based Data-Rate Processors

B. Bharathi Kannan;Srinivasan Sriramulu;Vidhya Gopal;M Karthick;Yanna Nithin Reddy;B Natarajan

Year: 2024 | Conference Paper |

Enhancing Stock Price Predictions Through LSTM-based Recurrent Neural Networks

G. V. Krishna Kumar;B. Natarajan;Venkatraman K;S. Gayathri Devi;Prabu Selvam;N. R. Nagarajan

2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)
Year: 2024 | Conference Paper |
Cited by: Papers (3)
Analyzing finances has become increasingly challenging in today’s investment landscape, where making valuable and informed investment decisions is crucial. The fluctuation of share prices plays a pivotal role in determining investors’ profits or losses. Current forecasting techniques encompass both linear and non-linear algorithms. However, these methods primarily emphasize forecasting changes in ...Show More

Enhancing Stock Price Predictions Through LSTM-based Recurrent Neural Networks

G. V. Krishna Kumar;B. Natarajan;Venkatraman K;S. Gayathri Devi;Prabu Selvam;N. R. Nagarajan

Year: 2024 | Conference Paper |

Deep Convolutional Neural Networks for Early-Stage Detection and Prognostication of Lung and Colon Cancer

Kumar Laxmikant;Arthi A;V Vinodhini;B Natarajan;R Bhuvaneswari;Prabu Selvam

2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
Lung and colon cancers are significant health issues across the world, prompting the need for inventive methods of diagnosis. This study takes the lead in introducing advanced Deep ConvNets (CNNs) to enhance the accuracy of early detection. The model is trained on extensive datasets, resulting in impressive outcomes. During training, it achieves an accuracy of 92.54% and a loss value of 0.0161. Th...Show More

Deep Convolutional Neural Networks for Early-Stage Detection and Prognostication of Lung and Colon Cancer

Kumar Laxmikant;Arthi A;V Vinodhini;B Natarajan;R Bhuvaneswari;Prabu Selvam

Year: 2024 | Conference Paper |

Efficient Underwater Object Detection Using Deep Neural Networks

Shaik Atheeq Rasool;R Annamalai;B. Natarajan

2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
The increasing amount of underwater visual data and deep sea research have led to the rise of marine animal identification as a major field for data processing and analysis. The need to protect the ecosystem emphasizes how important this endeavor is, yet it’s still difficult because of things like the complexities of underwater backdrops, poor picture and video quality, and the variety of movement...Show More

Efficient Underwater Object Detection Using Deep Neural Networks

Shaik Atheeq Rasool;R Annamalai;B. Natarajan

Year: 2024 | Conference Paper |

On Zero-Dynamics Stealthy Attacks with Learned State Space Models

Stephanie Harshbarger;Balasubramaniam Natarajan;Eugene Y. Vasserman;Muhammad Farooq Umar;Mohammad Shadmand;George Amariucai

2024 4th International Conference on Smart Grid and Renewable Energy (SGRE)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
Zero-dynamics stealthy attacks are a subset of false data injection attacks (FDIAs) that can be catastrophic as they are designed to be undetectable by traditional residual based anomaly detectors. The attacker’s ability to successfully attack a system relies heavily on their capacity to learn an accurate state space model. Utilizing a grey box approach to system identification, we show that even ...Show More

On Zero-Dynamics Stealthy Attacks with Learned State Space Models

Stephanie Harshbarger;Balasubramaniam Natarajan;Eugene Y. Vasserman;Muhammad Farooq Umar;Mohammad Shadmand;George Amariucai

Year: 2024 | Conference Paper |

Challenges and Opportunities in Deep Reinforcement Learning With Graph Neural Networks: A Comprehensive Review of Algorithms and Applications

Sai Munikoti;Deepesh Agarwal;Laya Das;Mahantesh Halappanavar;Balasubramaniam Natarajan

IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Volume: 35, Issue: 11 | Journal Article |
Cited by: Papers (35)
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation systems, and gaming. Similarly, graph neural networks (GNNs) have also demonstrated their superior performance in supervised learning for graph-structured data. In recent times, the fusion of GNN with DRL for graph-structured environments has attracte...Show More

Challenges and Opportunities in Deep Reinforcement Learning With Graph Neural Networks: A Comprehensive Review of Algorithms and Applications

Sai Munikoti;Deepesh Agarwal;Laya Das;Mahantesh Halappanavar;Balasubramaniam Natarajan

Year: 2024 | Volume: 35, Issue: 11 | Journal Article |

Towards Fluent Expression: The Impact of Deep Learning in Grammar Correction

Jayant Toleti;Amirthavarshini V;B Natarajan;R Elakkiya

2023 Fourth International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE)
Year: 2023 | Conference Paper |
Grammatical Error Correction is a crucial task in Natural Language Processing. This paper discusses the evolution of grammar error correction models from early rule-based approaches to advanced deep learning-based methodologies. This work utilizes the NAIST Lang-8 dataset, a valuable dataset offering parallel data, facilitating more straightforward evaluation and eliminating the need for additiona...Show More

Towards Fluent Expression: The Impact of Deep Learning in Grammar Correction

Jayant Toleti;Amirthavarshini V;B Natarajan;R Elakkiya

Year: 2023 | Conference Paper |

Gesture Controlled Virtual Mouse

Aditya Bansal;I R Oviya;B Natarajan;R Elakkiya

2023 Seventh International Conference on Image Information Processing (ICIIP)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Human mobility is restricted due to many causes like old age, health conditions, accidents etc. The present paper introduces an innovative approach for interacting with a virtual mouse through hand gestures, leveraging the combined capabilities of MediaPipe, OpenCV, and PyAutoGUI and its capability in identifying hand gestures and translating them into actionable commands. OpenCV enhances gesture ...Show More

Gesture Controlled Virtual Mouse

Aditya Bansal;I R Oviya;B Natarajan;R Elakkiya

Year: 2023 | Conference Paper |

Enhancing the Resolution of ChestX-ray Images with SRGAN and Sub-Pixel CNN

K Lokeshwar Reddy;S Sudharson;B Natarajan;S Theetchenya

2023 Seventh International Conference on Image Information Processing (ICIIP)
Year: 2023 | Conference Paper |
A crucial diagnostic tool for respiratory and heart disorders is chest X-ray imaging. However, the limited resolution of acquired images can hinder accurate interpretation and subsequent treatment decisions. To address this challenge, we employ deep learning-based super-resolution techniques, specifically SRGAN and ESPCN, to enhance the chest X-ray images. In this study, we proposed a modified ESP...Show More

Enhancing the Resolution of ChestX-ray Images with SRGAN and Sub-Pixel CNN

K Lokeshwar Reddy;S Sudharson;B Natarajan;S Theetchenya

Year: 2023 | Conference Paper |

SignSpeak: Exploring the Sign Language Communication Using Deep Neural Networks

Bhumaraju Mani Teja;B Natarajan;Elakkiya R;R Bhuvaneswari

2023 International Conference on Modeling, Simulation & Intelligent Computing (MoSICom)
Year: 2023 | Conference Paper |
This research’s primary goal is to create a system for detecting sign language. Sign language has long been accepted as a genuine mode of communication for people who are having hearing-impaired problems. The inability of technology to detect and comprehend sign language has limited its usage in daily life. This research will look at many ways to sign language detection, such as image and video pr...Show More

SignSpeak: Exploring the Sign Language Communication Using Deep Neural Networks

Bhumaraju Mani Teja;B Natarajan;Elakkiya R;R Bhuvaneswari

Year: 2023 | Conference Paper |

Outperforming Cryptocurrency Price Prediction Using Deep Reinforcement Learning Approach

P V R Subba Rao;B Natarajan;R Bhuvaneswari;Vidyabharathi Dakshinamurthi;Oviya I R;Syed Husain

2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Cryptocurrency price prediction is a complex and dynamic task. Cryptocurrencies are highly volatile digital assets impacted by a wide range of variables, including investor mood, legislative changes, market demand, and technical breakthroughs. This research presents a novel approach to cryptocurrency price prediction using Deep Reinforcement Learning (DRL). The proposed model incorporates proximal...Show More

Outperforming Cryptocurrency Price Prediction Using Deep Reinforcement Learning Approach

P V R Subba Rao;B Natarajan;R Bhuvaneswari;Vidyabharathi Dakshinamurthi;Oviya I R;Syed Husain

Year: 2023 | Conference Paper |

Automated Stock Trading using Reinforcement Learning

I Varshini Devi;B Natarajan;S Prabu;R Anandha Praba;K Ushanandhini;K. S. Guruprakash

2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Trading stocks requires effective methodologies to succeed in the business process. Automated stock trading involves utilizing computer algorithms and software programs to execute stock market trades automatically. This research work proposes an ensemble approach that leverages deep reinforcement learning to discover a stock trading strategy aimed at maximizing investment returns. By employing thr...Show More

Automated Stock Trading using Reinforcement Learning

I Varshini Devi;B Natarajan;S Prabu;R Anandha Praba;K Ushanandhini;K. S. Guruprakash

Year: 2023 | Conference Paper |

An Efficient Approach for Obstacle Avoidance and Navigation in Robots

M Phani Shanmukh;B Natarajan;C. Kannan;M. Tamilselvi;T. Vigneshwaran;S. Syed Husain

2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Reinforcement learning has emerged as a prominent technique for enhancing robot obstacle avoidance capabilities in recent years. This research provides a comprehensive overview of reinforcement learning methods, focusing on Bayesian, static, dynamic policy, Deep Q-Learning (DQN) and extended dynamic policy algorithms. In the context of robot obstacle avoidance, these algorithms enable an agent to ...Show More

An Efficient Approach for Obstacle Avoidance and Navigation in Robots

M Phani Shanmukh;B Natarajan;C. Kannan;M. Tamilselvi;T. Vigneshwaran;S. Syed Husain

Year: 2023 | Conference Paper |

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